{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.154847Z",
     "start_time": "2024-11-06T19:12:26.290930Z"
    }
   },
   "source": [
    "import concurrent.futures\n",
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "from virtual_lab.agent import Agent\n",
    "from virtual_lab.constants import CONSISTENT_TEMPERATURE, CREATIVE_TEMPERATURE\n",
    "from virtual_lab.prompts import (\n",
    "    CODING_RULES,\n",
    "    REWRITE_PROMPT,\n",
    "    SCIENTIFIC_CRITIC,\n",
    "    create_merge_prompt,\n",
    ")\n",
    "from virtual_lab.run_meeting import run_meeting\n",
    "from virtual_lab.utils import load_summaries"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "cc186057c8dcc3a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.164544Z",
     "start_time": "2024-11-06T19:12:28.161145Z"
    }
   },
   "source": [
    "# Set up key parameters\n",
    "num_iterations = 5\n",
    "num_rounds = 3\n",
    "save_dir = Path(\"discussions\")\n",
    "model = \"gpt-4o-2024-08-06\"\n",
    "background_prompt = \"You are working on a research project to use machine learning to develop antibodies or nanobodies for the newest variant of the SARS-CoV-2 spike protein that also, ideally, have activity against other circulating minor variants and past variants.\""
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Team selection",
   "id": "a9259ddc2710488f"
  },
  {
   "cell_type": "code",
   "id": "c55d79aa92e377b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.273351Z",
     "start_time": "2024-11-06T19:12:28.269565Z"
    }
   },
   "source": [
    "# Team selection - prompts\n",
    "team_selection_dir = save_dir / \"team_selection\"\n",
    "\n",
    "team_selection_agenda = f\"\"\"{background_prompt} You need to select a team of three scientists to help you with this project. Please select the team members that you would like to invite to a discussion to create the antibody/nanobody design approach. Please list the team members in the following format, using the team member below as an example. You should not include yourself (Principal Investigator) in the list.\n",
    "\n",
    "Agent(\n",
    "    title=\"Principal Investigator\",\n",
    "    expertise=\"applying artificial intelligence to biomedical research\",\n",
    "    goal=\"perform research in your area of expertise that maximizes the scientific impact of the work\",\n",
    "    role=\"lead a team of experts to solve an important problem in artificial intelligence for biomedicine, make key decisions about the project direction based on team member input, and manage the project timeline and resources\",\n",
    ")\n",
    "\"\"\""
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.297323Z",
     "start_time": "2024-11-06T19:12:28.294152Z"
    }
   },
   "cell_type": "code",
   "source": [
    "PRINCIPAL_INVESTIGATOR = Agent(\n",
    "    title=\"Principal Investigator\",\n",
    "    expertise=\"applying artificial intelligence to biomedical research\",\n",
    "    goal=\"perform research in your area of expertise that maximizes the scientific impact of the work\",\n",
    "    role=\"lead a team of experts to solve an important problem in artificial intelligence for biomedicine, make key decisions about the project direction based on team member input, and manage the project timeline and resources\",\n",
    ")"
   ],
   "id": "5c2a20e3352662c1",
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "50722e4dadc135d8",
   "metadata": {},
   "source": [
    "# Team selection - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"individual\",\n",
    "            team_member=PRINCIPAL_INVESTIGATOR,\n",
    "            agenda=team_selection_agenda,\n",
    "            save_dir=team_selection_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "id": "f1c023cebeaaf883",
   "metadata": {},
   "source": [
    "# Team selection - merge\n",
    "team_selection_summaries = load_summaries(discussion_paths=list(team_selection_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(team_selection_summaries)}\")\n",
    "\n",
    "team_selection_merge_prompt = create_merge_prompt(agenda=team_selection_agenda)\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=PRINCIPAL_INVESTIGATOR,\n",
    "    summaries=team_selection_summaries,\n",
    "    agenda=team_selection_merge_prompt,\n",
    "    save_dir=team_selection_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "id": "3d9186ce198c1b0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.315845Z",
     "start_time": "2024-11-06T19:12:28.311103Z"
    }
   },
   "source": [
    "# Add team members\n",
    "IMMUNOLOGIST = Agent(\n",
    "    title=\"Immunologist\",\n",
    "    expertise=\"antibody engineering and immune response characterization\",\n",
    "    goal=\"guide the development of antibodies/nanobodies that elicit a strong and broad immune response\",\n",
    "    role=\"advise on immunogenicity, cross-reactivity with other variants, and potential for therapeutic application, ensuring the designs are viable for experimental validation and downstream applications\",\n",
    ")\n",
    "\n",
    "MACHINE_LEARNING_SPECIALIST = Agent(\n",
    "    title=\"Machine Learning Specialist\",\n",
    "    expertise=\"developing algorithms for protein-ligand interactions and optimization\",\n",
    "    goal=\"create and apply machine learning models to predict antibody efficacy and optimize binding affinity across SARS-CoV-2 variants\",\n",
    "    role=\"lead the development of AI tools for predicting interactions and refining antibody designs based on computational results\",\n",
    ")\n",
    "\n",
    "COMPUTATIONAL_BIOLOGIST = Agent(\n",
    "    title=\"Computational Biologist\",\n",
    "    expertise=\"protein structure prediction and molecular dynamics simulations\",\n",
    "    goal=\"develop predictive models to identify potential antibody/nanobody candidates and simulate interactions with the SARS-CoV-2 spike protein\",\n",
    "    role=\"provide insights into structural dynamics, guide virtual screening efforts, and validate computational predictions with simulations\",\n",
    ")\n",
    "\n",
    "team_members = (\n",
    "    IMMUNOLOGIST,\n",
    "    MACHINE_LEARNING_SPECIALIST,\n",
    "    COMPUTATIONAL_BIOLOGIST,\n",
    "    SCIENTIFIC_CRITIC,\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Projects specification",
   "id": "3804141d0b26537"
  },
  {
   "cell_type": "code",
   "id": "9df84a9e45d31bbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.346164Z",
     "start_time": "2024-11-06T19:12:28.339681Z"
    }
   },
   "source": [
    "# Project specification - prompts\n",
    "project_specification_dir = save_dir / \"project_specification\"\n",
    "\n",
    "project_specification_agenda = f\"{background_prompt} Please create an antibody/nanobody design approach to solve this problem. Decide whether you will design antibodies or nanobodies. For your choice, decide whether you will design the antibodies/nanobodies de novo or whether you will modify existing antibodies/nanobodies. If modifying existing antibodies/nanobodies, please specify which antibodies/nanobodies to start with as good candidates for targeting the newest variant of the SARS-CoV-2 spike protein. If designing antibodies/nanobodies de novo, please describe how you will propose antibody/nanobody candidates.\"\n",
    "\n",
    "project_specification_questions = (\n",
    "    \"Will you design standard antibodies or nanobodies?\",\n",
    "    \"Will you design antibodies/nanobodies de novo or will you modify existing antibodies/nanobodies (choose only one)?\",\n",
    "    \"If modifying existing antibodies/nanobodies, which precise antibodies/nanobodies will you modify (please list 3-4)?\",\n",
    "    \"If designing antibodies/nanobodies de novo, how exactly will you propose antibody/nanobody candidates?\",\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "9ff509ba8824b8ac",
   "metadata": {},
   "source": [
    "# Project specification - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"team\",\n",
    "            team_lead=PRINCIPAL_INVESTIGATOR,\n",
    "            team_members=team_members,\n",
    "            agenda=project_specification_agenda,\n",
    "            agenda_questions=project_specification_questions,\n",
    "            save_dir=project_specification_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "            num_rounds=num_rounds,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "id": "e66e0cfa85f2176d",
   "metadata": {},
   "source": [
    "# Project specification - merge\n",
    "project_specification_summaries = load_summaries(discussion_paths=list(project_specification_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(project_specification_summaries)}\")\n",
    "\n",
    "project_specification_merge_prompt = create_merge_prompt(\n",
    "    agenda=project_specification_agenda,\n",
    "    agenda_questions=project_specification_questions,\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=PRINCIPAL_INVESTIGATOR,\n",
    "    summaries=project_specification_summaries,\n",
    "    agenda=project_specification_merge_prompt,\n",
    "    save_dir=project_specification_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    "    num_rounds=num_rounds,\n",
    ")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.365090Z",
     "start_time": "2024-11-06T19:12:28.361921Z"
    }
   },
   "cell_type": "code",
   "source": "nanobody_prompt = \"Your team previous decided to modify existing nanobodies to improve their binding to the newest variant of the SARS-CoV-2 spike protein.\"",
   "id": "7a6be070a3f8a14a",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Tools Selection",
   "id": "7e5307d354d28011"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.382762Z",
     "start_time": "2024-11-06T19:12:28.376659Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Tools selection - prompts\n",
    "tools_selection_dir = save_dir / \"tools_selection\"\n",
    "\n",
    "tools_selection_agenda = f\"{background_prompt} {nanobody_prompt} Now you need to select machine learning and/or computational tools to implement this nanobody design approach. Please list several tools (5-10) that would be relevant to this nanobody design approach and how they could be used in the context of this project. If selecting machine learning tools, please prioritize pre-trained models (e.g., pre-trained protein language models or protein structure prediction models) for simplicity.\"\n",
    "\n",
    "tools_selection_questions = (\n",
    "    \"What machine learning and/or computational tools could be used for this nanobody design approach (list 5-10)?\",\n",
    "    \"For each tool, how could it be used for designing modified nanobodies?\",\n",
    ")\n",
    "\n",
    "tools_selection_prior_summaries = load_summaries(discussion_paths=[project_specification_dir / \"merged.json\"])\n",
    "print(f\"Number of prior summaries: {len(tools_selection_prior_summaries)}\")"
   ],
   "id": "a001a2ca1ea13a4c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of prior summaries: 1\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Tools selection - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"team\",\n",
    "            team_lead=PRINCIPAL_INVESTIGATOR,\n",
    "            team_members=team_members,\n",
    "            summaries=tools_selection_prior_summaries,\n",
    "            agenda=tools_selection_agenda,\n",
    "            agenda_questions=tools_selection_questions,\n",
    "            save_dir=tools_selection_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "            num_rounds=num_rounds,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "id": "a95de22d2f1d277b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Tools selection - merge\n",
    "tools_selection_summaries = load_summaries(discussion_paths=list(tools_selection_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(tools_selection_summaries)}\")\n",
    "\n",
    "tools_selection_merge_prompt = create_merge_prompt(\n",
    "    agenda=tools_selection_agenda,\n",
    "    agenda_questions=tools_selection_questions,\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=PRINCIPAL_INVESTIGATOR,\n",
    "    summaries=tools_selection_summaries,\n",
    "    agenda=tools_selection_merge_prompt,\n",
    "    save_dir=tools_selection_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    "    num_rounds=num_rounds,\n",
    ")"
   ],
   "id": "580378a119b0cce5",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Implementation",
   "id": "c3777aacd05e6e17"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:25:54.904553Z",
     "start_time": "2024-11-06T19:25:54.884088Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Implementation - prompts\n",
    "implementation_agent_selection_dir = save_dir / \"implementation_agent_selection\"\n",
    "\n",
    "implementation_agent_selection_agenda = f\"{background_prompt} {nanobody_prompt} Your team needs to build three components of a nanobody design pipeline: ESM, AlphaFold-Multimer, and Rosetta. For each component, please select the team member who will implement the component. A team member may implement more than one component.\"\n",
    "\n",
    "implementation_agent_selection_questions = (\n",
    "    \"Which team member will implement ESM?\",\n",
    "    \"Which team member will implement AlphaFold-Multimer?\",\n",
    "    \"Which team member will implement Rosetta?\",\n",
    ")\n",
    "\n",
    "implementation_agent_selection_prior_summaries = load_summaries(discussion_paths=[team_selection_dir / \"merged.json\", project_specification_dir / \"merged.json\", tools_selection_dir / \"merged.json\"])\n",
    "print(f\"Number of prior summaries: {len(implementation_agent_selection_prior_summaries)}\")"
   ],
   "id": "a4b45d33467d1405",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of prior summaries: 3\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Implementation - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"individual\",\n",
    "            team_member=PRINCIPAL_INVESTIGATOR,\n",
    "            summaries=implementation_agent_selection_prior_summaries,\n",
    "            agenda=implementation_agent_selection_agenda,\n",
    "            agenda_questions=implementation_agent_selection_questions,\n",
    "            save_dir=implementation_agent_selection_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "id": "777c7f17e1853145",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Implementation - merge\n",
    "implementation_agent_selection_summaries = load_summaries(discussion_paths=list(implementation_agent_selection_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(implementation_agent_selection_summaries)}\")\n",
    "\n",
    "implementation_agent_selection_merge_prompt = create_merge_prompt(\n",
    "    agenda=implementation_agent_selection_agenda,\n",
    "    agenda_questions=implementation_agent_selection_questions\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=PRINCIPAL_INVESTIGATOR,\n",
    "    summaries=implementation_agent_selection_summaries,\n",
    "    agenda=implementation_agent_selection_merge_prompt,\n",
    "    save_dir=implementation_agent_selection_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "b741ab035e21003c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### ESM",
   "id": "9b1ac62cf9a8f39"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.463774Z",
     "start_time": "2024-11-06T19:12:28.460121Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# ESM - prompts\n",
    "esm_dir = save_dir / \"esm\"\n",
    "\n",
    "esm_agenda = f\"{background_prompt} {nanobody_prompt} Now you must use ESM to suggest modifications to an existing antibody. Please write a complete Python script that takes a nanobody sequence as input and uses ESM amino acid log-likelihoods to identify the most promising point mutations by log-likelihood ratio.\""
   ],
   "id": "1b2b6e3a180a548e",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# ESM - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"individual\",\n",
    "            team_member=MACHINE_LEARNING_SPECIALIST,\n",
    "            agenda=esm_agenda,\n",
    "            agenda_rules=CODING_RULES,\n",
    "            save_dir=esm_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "            num_rounds=num_rounds,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "id": "e0affb5cd00bec96",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# ESM - merge\n",
    "esm_summaries = load_summaries(discussion_paths=list(esm_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(esm_summaries)}\")\n",
    "\n",
    "esm_merge_prompt = create_merge_prompt(\n",
    "    agenda=esm_agenda,\n",
    "    agenda_rules=CODING_RULES,\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=MACHINE_LEARNING_SPECIALIST,\n",
    "    summaries=esm_summaries,\n",
    "    agenda=esm_merge_prompt,\n",
    "    save_dir=esm_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "6b0936e225dcc32f",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Improve ESM",
   "id": "9cf7a7a3d2b7b7fe"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.507880Z",
     "start_time": "2024-11-06T19:12:28.504069Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Improve ESM - prompts\n",
    "improve_esm_agenda = f\"\"\"You previously wrote a Python script that uses ESM to compute the log-likelihood ratio of point mutations in a nanobody sequence (see summary). {REWRITE_PROMPT}\n",
    "\n",
    "1. Replace \"facebook/esm1b-t33_650M_UR50S\" with \"facebook/esm1b_t33_650M_UR50S\".\n",
    "2. Run the calculations of the mutant log-likelihoods by iterating through the sequences in batches of 16.\n",
    "3. Add a progress bar to the batched mutant log-likelihood calculations.\n",
    "4. Run the mutant log-likelihood calculations on CUDA but with no gradients.\n",
    "5. Load the nanobody sequence from a user-specified CSV file that has the columns \"sequence\" and \"name\". Adapt your code to run the mutant log-likelihood calculations on all sequences in the CSV file one-by-one.\n",
    "6. For each sequence, save the mutant log-likelihoods to a CSV file with the format \"mutated_sequence,position,original_aa,mutated_aa,log_likelihood_ratio\". Ask the user for a save directory and then save this CSV file in that directory with the name: <nanbody-name>.csv.\"\"\""
   ],
   "id": "b062e9cb5f92a482",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Improve ESM - discussion\n",
    "improve_esm_summaries = load_summaries(discussion_paths=list(esm_dir.glob(\"merged.json\")))\n",
    "print(f\"Number of summaries: {len(improve_esm_summaries)}\")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=MACHINE_LEARNING_SPECIALIST,\n",
    "    summaries=improve_esm_summaries,\n",
    "    agenda=improve_esm_agenda,\n",
    "    save_dir=esm_dir,\n",
    "    save_name=\"improved\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "6388c95f9e77d811",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### AlphaFold-Multimer",
   "id": "9ba22302ea18a575"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:28.530133Z",
     "start_time": "2024-11-06T19:12:28.526354Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# AlphaFold-Multimer - prompts\n",
    "alphafold_dir = save_dir / \"alphafold\"\n",
    "\n",
    "alphafold_agenda = f\"{background_prompt} {nanobody_prompt} Now you must use AlphaFold-Multimer to predict the structure of a nanobody-antigen complex and evaluate its binding. I will run AlphaFold-Multimer on several nanobody-antigen complexes and you need to process the outputs. Please write a complete Python script that takes as input a directory containing PDB files where each PDB file contains one nanobody-antigen complex predicted by AlphaFold-Multimer and outputs a CSV file containing the AlphaFold-Multimer confidence of each nanobody-antigen complex in terms of the interface pLDDT.\""
   ],
   "id": "9588993af806c3f1",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# AlphaFold-Multimer - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"individual\",\n",
    "            team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "            agenda=alphafold_agenda,\n",
    "            agenda_rules=CODING_RULES,\n",
    "            save_dir=alphafold_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "            num_rounds=num_rounds,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "id": "1ca49b4e29ac8b6a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# AlphaFold-Multimer - merge\n",
    "alphafold_summaries = load_summaries(discussion_paths=list(alphafold_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(alphafold_summaries)}\")\n",
    "\n",
    "alphafold_merge_prompt = create_merge_prompt(\n",
    "    agenda=alphafold_agenda,\n",
    "    agenda_rules=CODING_RULES,\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "    summaries=alphafold_summaries,\n",
    "    agenda=alphafold_merge_prompt,\n",
    "    save_dir=alphafold_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "d03e25b1ecad9557",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Improve AlphaFold-Multimer",
   "id": "619858f6ff3335c6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:30.394607Z",
     "start_time": "2024-11-06T19:12:30.391293Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Improve AlphaFold-Multimer - prompts\n",
    "improve_alphafold_agenda = f\"\"\"You previously wrote a Python script that processes the outputs of AlphaFold-Multimer to calculate the confidence of nanobody-antigen complexes (see summary). {REWRITE_PROMPT}\n",
    "\n",
    "1. Replace the current imports of Chain and Residue with \"from Bio.PDB.Chain import Chain\" and \"from Bio.PDB.Residue import Residue\".\n",
    "2. Remove the logging setup and simply print any log messages to the console.\n",
    "3. Replace the parallel processing with sequential processing to avoid getting an \"OSError: Too many open files\".\n",
    "4. Change the list of pdb_files to instead get all PDB files in the directory that follow the pattern \"**/*unrelaxed_rank_001*.pdb\".\n",
    "5. Change the calculation of average pLDDT to divide by the number of atoms rather than the number of residues.\n",
    "6. Return and save in the CSV both the number of residues and the number of atoms in the interface.\n",
    "7. Change the default distance threshold to 4.\"\"\""
   ],
   "id": "dd78a537e8608abb",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Improve AlphaFold-Multimer - discussion\n",
    "improve_alphafold_summaries = load_summaries(discussion_paths=list(alphafold_dir.glob(\"merged.json\")))\n",
    "print(f\"Number of summaries: {len(improve_alphafold_summaries)}\")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "    summaries=improve_alphafold_summaries,\n",
    "    agenda=improve_alphafold_agenda,\n",
    "    save_dir=alphafold_dir,\n",
    "    save_name=\"improved\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "9d9bf62680e027cd",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Rosetta",
   "id": "7ba34f13b0ec3889"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:32.224773Z",
     "start_time": "2024-11-06T19:12:32.221411Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Rosetta - prompts\n",
    "rosetta_dir = save_dir / \"rosetta\"\n",
    "\n",
    "rosetta_agenda = f\"{background_prompt} {nanobody_prompt} Now you must use Rosetta to calculate the binding energy of nanobody-antigen complexes. You must do this in three parts. First, write a complete RosettaScripts XML file that calculates the binding energy of a nanobody-antigen complex as provided in PDB format, including any necessary preprocessing steps for the complex. Second, write an example command that uses Rosetta to run this RosettaScripts XML file on a given PDB file to calculate the binding energy and save it to a score file. Third, write a complete Python script that takes as input a directory with multiple Rosetta binding energy score files and outputs a single CSV file with the names and scores of each of the individual files in sorted order (highest to lowest score).\""
   ],
   "id": "38b7c084b07dd169",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Rosetta - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"individual\",\n",
    "            team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "            agenda=rosetta_agenda,\n",
    "            agenda_rules=CODING_RULES,\n",
    "            save_dir=rosetta_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "            num_rounds=num_rounds,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "id": "3433242b9472cd28",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Rosetta - merge\n",
    "rosetta_summaries = load_summaries(discussion_paths=list(rosetta_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(rosetta_summaries)}\")\n",
    "\n",
    "rosetta_merge_prompt = create_merge_prompt(\n",
    "    agenda=rosetta_agenda,\n",
    "    agenda_rules=CODING_RULES,\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "    summaries=rosetta_summaries,\n",
    "    agenda=rosetta_merge_prompt,\n",
    "    save_dir=rosetta_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "4563b6d0b6116f2a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Improve Rosetta",
   "id": "4771524f6b0c8270"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:34.078655Z",
     "start_time": "2024-11-06T19:12:34.075618Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Improve Rosetta XML - prompts\n",
    "improve_rosetta_xml_agenda = f\"\"\"You previously wrote a RosettaScripts XML file to calculate the binding affinity of a nanobody-antigen complex (see summary). {REWRITE_PROMPT}\n",
    "\n",
    "1. Replace \"ref15.wts\" with \"ref2015.wts\".\n",
    "2. Remove the InterfaceEnergy filter since it is not valid in Rosetta.\n",
    "3. Replace the entire output tag (including any nested tags) with <OUTPUT scorefxn=\"ref15\"/>.\"\"\""
   ],
   "id": "a03ce8ff3dc2c718",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Improve Rosetta XML - discussion\n",
    "improve_rosetta_xml_summaries = load_summaries(discussion_paths=list(rosetta_dir.glob(\"merged.json\")))\n",
    "print(f\"Number of summaries: {len(improve_rosetta_xml_summaries)}\")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "    summaries=improve_rosetta_xml_summaries,\n",
    "    agenda=improve_rosetta_xml_agenda,\n",
    "    save_dir=rosetta_dir,\n",
    "    save_name=\"improved_xml\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "d92fb27857e97f52",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:35.447920Z",
     "start_time": "2024-11-06T19:12:35.443981Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Improve Rosetta Python - prompts\n",
    "improve_rosetta_python_agenda = f\"\"\"You previously wrote a Python script to aggregate multiple Rosetta binding energy score files into one CSV file (see summary). {REWRITE_PROMPT}\n",
    "\n",
    "1. Modify the extract_scores_from_file function so that it extracts the dG_separated value from a file of the following form.\n",
    "\n",
    "SEQUENCE:\n",
    "SCORE: total_score complex_normalized           dG_cross dG_cross/dSASAx100 dG_separated dG_separated/dSASAx100 dSASA_hphobic dSASA_int dSASA_polar delta_unsatHbonds dslf_fa13    fa_atr    fa_dun   fa_elec fa_intra_rep fa_intra_sol_xover4              fa_rep              fa_sol hbond_E_fraction hbond_bb_sc hbond_lr_bb    hbond_sc hbond_sr_bb hbonds_int lk_ball_wtd    nres_all    nres_int       omega     p_aa_pp    packstat per_residue_energy_int pro_close rama_prepro         ref    sc_value side1_normalized side1_score side2_normalized side2_score yhh_planarity description\n",
    "SCORE:    -990.807             -2.914            -21.436             -1.857      -21.436                 -1.857       774.274  1154.088     379.813            12.000    -3.867 -1928.622   376.416  -541.777        3.745              54.944             265.303            1052.322            0.053     -84.023    -130.532     -54.069     -46.266      1.000     -41.725     340.000      55.000      39.977     -81.331       0.000                 -2.699     2.349      -6.870     131.513       0.000           -2.236     -51.431           -3.031     -97.008         1.706 KP3_Ty1-G59Y_unrelaxed_rank_001_alphafold2_multimer_v3_model_3_seed_000_0001\"\"\""
   ],
   "id": "48ed12daeae0cf1e",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Improve Rosetta Python - discussion\n",
    "improve_rosetta_python_summaries = load_summaries(discussion_paths=list(rosetta_dir.glob(\"merged.json\")))\n",
    "print(f\"Number of summaries: {len(improve_rosetta_python_summaries)}\")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=COMPUTATIONAL_BIOLOGIST,\n",
    "    summaries=improve_rosetta_python_summaries,\n",
    "    agenda=improve_rosetta_python_agenda,\n",
    "    save_dir=rosetta_dir,\n",
    "    save_name=\"improved_python\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "ea4287f833216ee9",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Workflow Design",
   "id": "a050fffc08866b10"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:12:37.217607Z",
     "start_time": "2024-11-06T19:12:37.213147Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Workflow design - prompts\n",
    "workflow_design_dir = save_dir / \"workflow_design\"\n",
    "\n",
    "workflow_design_agenda = f\"{background_prompt} {nanobody_prompt} Your team has built three components of a nanobody design pipeline: ESM, AlphaFold-Multimer, and Rosetta. Each of these three tools can be used to score a nanobody mutation based on how the mutation affects binding to an antigen. Your goal is to start with an existing nanobody and iteratively add mutations to it to improve its binding to the newest variant of the SARS-CoV-2 spike protein, resulting in 24 modified nanobodies with one or more mutations. Please determine how to use ESM, AlphaFold-Multimer, and Rosetta in this iterative design process. An important constraint is that ESM can evaluate all potential mutations to a nanobody in 5 minutes while AlphaFold-Multimer takes 30 minutes per mutation and Rosetta takes five minutes per mutation. The whole iterative process should take no more than a few days to complete. Note that AlphaFold-Multimer must be run before Rosetta on each mutation since Rosetta requires the nanobody-antigen structure predicted by AlphaFold-Multimer. Additionally, note that ESM log-likelihood ratios are generally in the range of 5-10 (higher is better), AlphaFold-Multimer interface pLDDT confidence scores are generally in the range of 60-80 (higher is better), and Rosetta binding energy scores are generally in the range of -20 to -40 (lower is better).\"\n",
    "\n",
    "workflow_design_questions = (\n",
    "    \"In each iteration, what is the order of operations for evaluating mutations with ESM, AlphaFold-Multimer, and Rosetta?\",\n",
    "    \"In each iteration, how many mutations (give a single number) will you evaluate with ESM, AlphaFold-Multimer, and Rosetta?\",\n",
    "    \"At the end of each iteration, how will you weigh the ESM, AlphaFold-Multimer, and/or Rosetta scores (give a formula) to rank the nanobody mutations?\",\n",
    "    \"At the end of each iteration, how many of the top-ranked mutations (give a single number) will you keep for the next round?\",\n",
    "    \"How will you decide how many iterations of mutations to run?\",\n",
    "    \"After all of the iterations are complete, how exactly (step-by-step) will you select the final set of 24 modified nanobodies from across the iterations for experimental validation?\",\n",
    ")"
   ],
   "id": "c55ec9669eeee9fe",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Workflow design - discussion\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    concurrent.futures.wait([\n",
    "        executor.submit(\n",
    "            run_meeting,\n",
    "            meeting_type=\"individual\",\n",
    "            team_member=PRINCIPAL_INVESTIGATOR,\n",
    "            agenda=workflow_design_agenda,\n",
    "            agenda_questions=workflow_design_questions,\n",
    "            save_dir=workflow_design_dir,\n",
    "            save_name=f\"discussion_{iteration_num + 1}\",\n",
    "            temperature=CREATIVE_TEMPERATURE,\n",
    "            model=model,\n",
    "        ) for iteration_num in range(num_iterations)\n",
    "    ])"
   ],
   "id": "3210bdc6d68bd0bc",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Workflow design - merge\n",
    "workflow_design_summaries = load_summaries(discussion_paths=list(workflow_design_dir.glob(\"discussion_*.json\")))\n",
    "print(f\"Number of summaries: {len(workflow_design_summaries)}\")\n",
    "\n",
    "workflow_design_merge_prompt = create_merge_prompt(\n",
    "    agenda=workflow_design_agenda,\n",
    "    agenda_questions=workflow_design_questions,\n",
    ")\n",
    "\n",
    "run_meeting(\n",
    "    meeting_type=\"individual\",\n",
    "    team_member=PRINCIPAL_INVESTIGATOR,\n",
    "    summaries=workflow_design_summaries,\n",
    "    agenda=workflow_design_merge_prompt,\n",
    "    save_dir=workflow_design_dir,\n",
    "    save_name=\"merged\",\n",
    "    temperature=CONSISTENT_TEMPERATURE,\n",
    "    model=model,\n",
    ")"
   ],
   "id": "cb4534d4913ecf88",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Virtual Lab Analysis",
   "id": "bb5b2c7839b930aa"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:38:20.981443Z",
     "start_time": "2024-11-06T19:38:20.978560Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "id": "74d3a3d37081ed02",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:38:21.120274Z",
     "start_time": "2024-11-06T19:38:21.116979Z"
    }
   },
   "cell_type": "code",
   "source": [
    "figure_dir = Path(\"figures/virtual_lab_analysis\")\n",
    "figure_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "phase_to_agent_to_word_count = {}"
   ],
   "id": "8bebeb39ba3c3b0b",
   "outputs": [],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:38:21.226133Z",
     "start_time": "2024-11-06T19:38:21.220200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Count words that the human user wrote\n",
    "phase_to_human_words = {\n",
    "    \"team_selection\":  [\n",
    "        background_prompt,\n",
    "        PRINCIPAL_INVESTIGATOR.prompt,\n",
    "        SCIENTIFIC_CRITIC.prompt,\n",
    "        team_selection_agenda.replace(f\"{background_prompt} \", \"\"),\n",
    "    ],\n",
    "    \"project_specification\": [\n",
    "        project_specification_agenda.replace(f\"{background_prompt} \", \"\"),\n",
    "        *project_specification_questions,\n",
    "        nanobody_prompt,\n",
    "    ],\n",
    "    \"tools_selection\": [\n",
    "        tools_selection_agenda.replace(f\"{background_prompt} {nanobody_prompt} \", \"\"),\n",
    "        *tools_selection_questions,\n",
    "    ],\n",
    "    \"implementation_agent_selection\": [\n",
    "        implementation_agent_selection_agenda.replace(f\"{background_prompt} {nanobody_prompt} \", \"\"),\n",
    "        *implementation_agent_selection_questions,\n",
    "    ],\n",
    "    \"esm\": [\n",
    "        esm_agenda.replace(f\"{background_prompt} {nanobody_prompt} \", \"\"),\n",
    "        improve_esm_agenda.replace(f\" {REWRITE_PROMPT}\", \"\"),\n",
    "    ],\n",
    "    \"alphafold\": [\n",
    "        alphafold_agenda.replace(f\"{background_prompt} {nanobody_prompt} \", \"\"),\n",
    "        improve_alphafold_agenda.replace(f\" {REWRITE_PROMPT}\", \"\"),\n",
    "    ],\n",
    "    \"rosetta\": [\n",
    "        rosetta_agenda.replace(f\"{background_prompt} {nanobody_prompt} \", \"\"),\n",
    "        improve_rosetta_xml_agenda.replace(f\" {REWRITE_PROMPT}\", \"\"),\n",
    "        improve_rosetta_python_agenda.replace(f\" {REWRITE_PROMPT}\", \"\"),\n",
    "    ],\n",
    "    \"workflow_design\": [\n",
    "        workflow_design_agenda.replace(f\"{background_prompt} {nanobody_prompt} \", \"\"),\n",
    "        *workflow_design_questions,\n",
    "    ],\n",
    "}\n",
    "\n",
    "for phase, human_words in phase_to_human_words.items():\n",
    "    phase_to_agent_to_word_count[phase] = {\"Human Researcher\": len(\" \".join(human_words).split())}"
   ],
   "id": "3577d08c72a2aeb",
   "outputs": [],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:38:21.941126Z",
     "start_time": "2024-11-06T19:38:21.874880Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Count words that the LLM agents wrote\n",
    "for phase_dir in [team_selection_dir, project_specification_dir, tools_selection_dir, implementation_agent_selection_dir, esm_dir, alphafold_dir, rosetta_dir, workflow_design_dir]:\n",
    "    phase_name = phase_dir.name\n",
    "\n",
    "    print(f\"Phase: {phase_name}\")\n",
    "\n",
    "    # Load the text written by each agent\n",
    "    agent_to_text = {}\n",
    "    for path in phase_dir.glob(\"*.json\"):\n",
    "        with open(path) as f:\n",
    "            discussion = json.load(f)\n",
    "\n",
    "        for message in discussion:\n",
    "            agent_to_text.setdefault(message[\"agent\"], []).append(message[\"message\"])\n",
    "\n",
    "    # Count the number of words written by each agent\n",
    "    for agent, text in agent_to_text.items():\n",
    "        if agent == \"User\":\n",
    "            continue\n",
    "\n",
    "        agent_to_text[agent] = \" \".join(text)\n",
    "        word_count = len(agent_to_text[agent].split())\n",
    "        phase_to_agent_to_word_count[phase_name][agent] = word_count\n",
    "\n",
    "# Print words by phase\n",
    "for phase in phase_to_agent_to_word_count:\n",
    "    print(f\"Phase: {phase}\")\n",
    "    for agent, word_count in phase_to_agent_to_word_count[phase].items():\n",
    "        print(f\"Number of words written by {agent}: {word_count:,}\")\n",
    "    print()\n",
    "\n",
    "# Sum word counts across phases\n",
    "agent_to_word_count = {}\n",
    "for phase in phase_to_agent_to_word_count:\n",
    "    for agent, word_count in phase_to_agent_to_word_count[phase].items():\n",
    "        agent_to_word_count[agent] = agent_to_word_count.get(agent, 0) + word_count\n",
    "\n",
    "# Total number of words written by each LLM agent\n",
    "for agent, word_count in agent_to_word_count.items():\n",
    "    print(f\"Total number of words written by {agent}: {word_count:,}\")\n",
    "\n",
    "print()\n",
    "\n",
    "# Total number of words written by all LLM agents\n",
    "total_human_words = sum(phase_to_agent_to_word_count[phase][\"Human Researcher\"] for phase in phase_to_agent_to_word_count)\n",
    "total_agent_words = sum(word_count for agent, word_count in agent_to_word_count.items() if agent != \"Human Researcher\")\n",
    "\n",
    "print(f\"Total number of words written by Human Researcher: {total_human_words:,}\")\n",
    "print(f\"Total number of words written by all LLM agents: {total_agent_words:,}\")"
   ],
   "id": "30b622f7378bd3ec",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phase: team_selection\n",
      "Phase: project_specification\n",
      "Phase: tools_selection\n",
      "Phase: implementation_agent_selection\n",
      "Phase: esm\n",
      "Phase: alphafold\n",
      "Phase: rosetta\n",
      "Phase: workflow_design\n",
      "Phase: team_selection\n",
      "Number of words written by Human Researcher: 294\n",
      "Number of words written by Principal Investigator: 1,296\n",
      "\n",
      "Phase: project_specification\n",
      "Number of words written by Human Researcher: 142\n",
      "Number of words written by Principal Investigator: 10,513\n",
      "Number of words written by Scientific Critic: 6,068\n",
      "Number of words written by Immunologist: 3,684\n",
      "Number of words written by Machine Learning Specialist: 4,186\n",
      "Number of words written by Computational Biologist: 4,277\n",
      "\n",
      "Phase: tools_selection\n",
      "Number of words written by Human Researcher: 91\n",
      "Number of words written by Principal Investigator: 11,035\n",
      "Number of words written by Scientific Critic: 6,318\n",
      "Number of words written by Immunologist: 3,566\n",
      "Number of words written by Machine Learning Specialist: 4,399\n",
      "Number of words written by Computational Biologist: 4,498\n",
      "\n",
      "Phase: implementation_agent_selection\n",
      "Number of words written by Human Researcher: 56\n",
      "Number of words written by Principal Investigator: 1,751\n",
      "\n",
      "Phase: esm\n",
      "Number of words written by Human Researcher: 179\n",
      "Number of words written by Machine Learning Specialist: 13,040\n",
      "Number of words written by Scientific Critic: 6,528\n",
      "\n",
      "Phase: alphafold\n",
      "Number of words written by Human Researcher: 216\n",
      "Number of words written by Computational Biologist: 13,052\n",
      "Number of words written by Scientific Critic: 6,200\n",
      "\n",
      "Phase: rosetta\n",
      "Number of words written by Human Researcher: 295\n",
      "Number of words written by Computational Biologist: 12,473\n",
      "Number of words written by Scientific Critic: 6,879\n",
      "\n",
      "Phase: workflow_design\n",
      "Number of words written by Human Researcher: 323\n",
      "Number of words written by Principal Investigator: 2,699\n",
      "\n",
      "Total number of words written by Human Researcher: 1,596\n",
      "Total number of words written by Principal Investigator: 27,294\n",
      "Total number of words written by Scientific Critic: 31,993\n",
      "Total number of words written by Immunologist: 7,250\n",
      "Total number of words written by Machine Learning Specialist: 21,625\n",
      "Total number of words written by Computational Biologist: 34,300\n",
      "\n",
      "Total number of words written by Human Researcher: 1,596\n",
      "Total number of words written by all LLM agents: 122,462\n"
     ]
    }
   ],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:38:22.773390Z",
     "start_time": "2024-11-06T19:38:22.768849Z"
    }
   },
   "cell_type": "code",
   "source": [
    "agent_to_color = {\n",
    "    agent: sns.color_palette(\"tab10\", n_colors=len(agent_to_word_count))[i]\n",
    "    for i, agent in enumerate(agent_to_word_count)\n",
    "}"
   ],
   "id": "62dd0609f9e7274c",
   "outputs": [],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:38:24.617038Z",
     "start_time": "2024-11-06T19:38:23.480892Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "ax.pie(\n",
    "    agent_to_word_count.values(),\n",
    "    labels=agent_to_word_count.keys(),\n",
    "    autopct=\"%1.1f%%\",\n",
    "    colors=[agent_to_color[agent] for agent in agent_to_word_count],\n",
    ")\n",
    "ax.set_title(f\"Words written\")\n",
    "plt.savefig(figure_dir / \"total_words_written.pdf\", bbox_inches=\"tight\")"
   ],
   "id": "3a00e6cdaa1a1c05",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-06T19:39:09.399864Z",
     "start_time": "2024-11-06T19:39:08.060143Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for phase in phase_to_agent_to_word_count:\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "    ax.pie(\n",
    "        phase_to_agent_to_word_count[phase].values(),\n",
    "        labels=phase_to_agent_to_word_count[phase].keys(),\n",
    "        autopct=\"%1.1f%%\",\n",
    "        colors=[agent_to_color[agent] for agent in phase_to_agent_to_word_count[phase]],\n",
    "    )\n",
    "    ax.set_title(f\"Words written in {phase.replace('_', ' ')}\")\n",
    "    plt.savefig(figure_dir / f\"{phase}_words_written.pdf\", bbox_inches=\"tight\")"
   ],
   "id": "8465196358c50d9e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "bdefc83347336dc3"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
